# Assessment function Function that performs computerized assessment ```r assessment( likelihood = NA, states, beta, eta, questioning_rule = "half_split", termination = "likelihood_maximization", SC = 0.5, N_items = NULL, ki = 1, textq = NULL, textr = NULL, repetition = FALSE, adaptive = TRUE, simulation = TRUE, interactiveplot = FALSE ) ``` ## Arguments - `likelihood`: A vector of the likelihood distribution on the states in the structure. - `states`: A state-by-problem matrix representing the structure, where an element is one if the item is included in the state, and zero otherwise. - `beta`: Vector of careless error probabilities. - `eta`: Vector of lucky guess error probabilities. - `questioning_rule`: A function which is used a questioning rules for the assessment. the default questioning rule is 'half_split'. - `termination`: Define and select one of the termination criteria: "likelihood_maximization" the assessment terminates when the likelihood of a knowledge state in a knowledge structure became higher of the termination criteria (Heller, and Repitsch, 2012). "items_discrimination" the assessment terminates if the marginal likelihood of all the items is outside the interval of the stopping criteria (Donadello, Spoto, Sambo, Badaloni, Granziol, Vidotto, 2017). - `SC`: The Stopping criterion for the assessment is a numeric vector of values between 0 and 1. When the "termination" parameter is "likelihood_maximization" this is a single scalar that corresponds to the likelihood that a knowledge state needed to terminates the assessment. When the "termination" parameter is "items_discrimination" this is a numeric vector of length two, the assessment terminate if the the marginal likelihood of each item is outside of the interval between the two elements. - `N_items`: Number of items in the test. Optional entry in case of adaptive = FALSE. - `ki`: A number indicating the row in the structure to simulate as the true knowledge states. - `textq`: A character vector containing the text of the questions. - `textr`: A list containing for each question the correct and incorrect answers. - `repetition`: Logical value. When the value is TRUE the assessment procedure is allowed to administer the same item more then one time. - `adaptive`: Logical value. When the value is TRUE the assessment proceed with an adaptive procedure, otherwise the items presentation is randomized - `simulation`: Logical value. When the value is TRUE the assessment proceed with simulating a complete assessment with a user knowledge state define in variable ki. Otherwise the assessment collect answer from the user. - `interactiveplot`: Logical value. When the value is TRUE the knowledge structure is plot using a color thermometer scale to represents the likelihood of the states. ## Returns The outcome of a single assessment. ## Examples ```r # Example 1: From Brancaccio,de Chiusole, Stefanutti (2023) # Consider the knowledge space and the parameters used in Brancaccio, # de Chiusole, Stefanutti (2023) in Example 1 states<-matrix(c( 0,0,0,0,0, 0,0,0,0,1, 0,0,1,0,1, 0,0,0,1,1, 0,0,1,1,1, 1,0,1,0,1, 0,1,0,1,1, 1,0,1,1,1, 0,1,1,1,1, 1,1,0,1,1, 1,1,1,1,1), byrow=TRUE, ncol=5) beta <-c(.004,.03,.02,.01,.007) eta <-c(5e-06, 5e-05, 4e-05,.007,.08) likelihood <-rep(1,nrow(states))/nrow(states) assessment(likelihood,states,beta,eta) # Example 2: Random items presentation assessment(N_items = 5 ,adaptive = FALSE) # Example 3: Interactive mode on in the console TextQuestion<-c("I frequently use computers.", "I know how to build an assessment instrument (i.e., a paper-and-pencil questionnaire).", "I can code and implement a 'for' loop (independently of its purpose).", "When you administer an adaptive assessment, each question: ", "I usually programme or code my assessment tools ", "I add my assessment in a programme (i.e , google form) ") TextResponse<-list( correct=list("Yes","Yes","Yes","depend from the previous questions","Yes","Yes"), incorrect=list(c("No"),c("No"),c("No"),c("is independent from the others questions", "depend from the gender of the interviewed"),c("No"),c("No")) ) map <- matrix(c(1,0,0,0, 0,1,0,0, 1,0,1,0, 0,1,0,1, 1,1,1,0, 1,1,0,0), ncol=4) skillmap<-cbind(1:6,map) states<-pks::delineate(skillmap)$K rownames(states)<-NULL eta <- rep(0.1,ncol(states)) beta <- rep(0.1,ncol(states)) likelihood <-rep(1,nrow(states))/nrow(states) if(interactive()){token <- assessment(likelihood,states,beta,eta,textq=TextQuestion, textr=TextResponse,simulation=FALSE)} ``` ## References Doignon, J.-P., & Falmagne, J.-C. (1999). Knowledge spaces. Springer. Donadello, I., Spoto, A., Sambo, F., Badaloni, S., Granziol, U., & Vidotto, G. (2017). ATS-PD: An adaptive testing system for psychological disorders. Educational and psychological measurement, 77(5), 792-815. Heller, J., & Repitsch, C. (2012). Exploiting prior information in stochastic knowledge assessment. Methodology.

assessment function